Prerequisite
If you want to build a product ChatGPT you’ll need a solid understanding of the Python language itself as well as some key libraries and concepts. Here are some prerequisites in Python that can help you get started with AI
Visit the links which is mentioned below, it will be redirected to official documentation and tutorials
- Python programming – Visit Here
- NumPy – Visit Here
- Pandas – Visit Here
- Matplotlib – Visit Here
- Scikit-learn – Visit Here
- TensorFlow – Visit Here
- Keras – Visit Here
- OpenCV – Visit Here
- PyTorch – Visit Here
- Hugging Face – Visit Here
- Caffe – Visit Here
Python programming
A strong foundation in Python programming is essential. You should be comfortable with basic programming concepts such as variables, data types, control structures, functions, classes OOPS Concepts with simple data structures like list, tuples, dictionaries, set along with their inbuilt methods
NumPy
NumPy is a Python library for numerical computing, and it provides a powerful array data structure and a variety of mathematical functions. Many machine learning algorithms require data to be represented as arrays, so you’ll need to be comfortable with NumPy to work with data in AI.
Pandas
Pandas is a Python library for data manipulation and analysis, and it provides data structures like dataframes, which are commonly used in data science and AI. You’ll need to be able to load and manipulate data using Pandas.
Matplotlib
Matplotlib is a Python library for creating visualizations, and it can be used to create plots and charts to help you understand your data.
Scikit-learn
Scikit-learn is a Python library for machine learning, and it provides a wide range of algorithms for classification, regression, clustering, and more. It’s a great library to start with when learning machine learning in Python.
TensorFlow
An open-source machine learning framework that enables developers to build and deploy ML models. It’s widely used in fields such as deep learning, computer vision, and natural language processing.
Keras
A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It’s designed to enable fast experimentation with deep neural networks
OpenCV
An open-source computer vision library, which provides a wide range of algorithms and tools for image and video processing. It’s widely used in fields such as robotics, augmented reality, and object detection.
PyTorch
An open-source machine learning library, developed by Facebook’s AI research group. It’s known for its ease of use and flexibility, and it’s widely used in fields such as computer vision, natural language processing, and reinforcement learning.
Hugging Face
An open-source natural language processing library, which provides a wide range of pre-trained models for tasks such as text classification, summarization, and question answering. It’s built on top of PyTorch and TensorFlow.
Caffe
An open-source deep learning framework, developed by Berkeley AI Research. It’s designed to be efficient and expressive, and it’s widely used in fields such as image classification and segmentation.
Conclusion
In summary, to learn artificial intelligence using Python & to build a great models like ChatGPT & DALL-E-2, you’ll need a solid foundation in Python programming, as well as knowledge of libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. If you want to work with deep learning, you’ll also need to learn a deep learning framework.